Update app.py

app.py

@@ -1,110 +1,27 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
 
-# Initialize the inference client
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+def show_exam_html():
+    with open("exam.html", "r") as file:
+        return file.read()
 
-# Define the response function
-def respond(message, history, system_message, max_tokens, temperature, top_p):
-    messages = [{"role": "system", "content": system_message}]
-    
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-    
-    response = ""
-    
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-        response += token
-        yield response
-
-# Define the Gradio interface
-demo = gr.Blocks()
-
-with demo:
-    # Load and display the HTML file
+def show_index_html():
     with open("index.html", "r") as file:
-        html_content = file.read()
-    
-    gr.HTML(html_content)
-    
-    # Create the chat interface
-    gr.ChatInterface(
-        respond,
-        additional_inputs=[
-            gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-            gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-            gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-            gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
-        ],
-    )
-
-# Launch the application
-if __name__ == "__main__":
-    demo.launch()
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-# Initialize the inference client
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-# Define the response function
-def respond(message, history, system_message, max_tokens, temperature, top_p):
-    messages = [{"role": "system", "content": system_message}]
-    
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+        return file.read()
 
-    messages.append({"role": "user", "content": message})
-    
-    response = ""
-    
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-        response += token
-        yield response
+def main():
+    with gr.Blocks() as demo:
+        gr.HTML(show_index_html())
 
-# Define the Gradio interface
-demo = gr.Blocks()
+        # A hidden HTML component to store and display exam.html content
+        exam_html = gr.HTML(show_exam_html(), visible=False)
+        
+        def start_exam():
+            return gr.update(visible=True), gr.update(visible=False)
+        
+        # Simulate the navigation event
+        gr.Button("Start Exam").click(start_exam, inputs=[], outputs=[exam_html, gr.HTML(show_index_html())])
 
-with demo:
-    # Load and display the HTML file
-    with open("index.html", "r") as file:
-        html_content = file.read()
-    
-    gr.HTML(html_content)
-    
-    # Create the chat interface
-    gr.ChatInterface(
-        respond,
-        additional_inputs=[
-            gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-            gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-            gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-            gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
-        ],
-    )
+    demo.launch()
 
-# Launch the application
 if __name__ == "__main__":
-    demo.launch()
+    main()